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kassambara

rstatix:Pipe-Friendly Framework for Basic Statistical Tests

Provides a simple and intuitive pipe-friendly framework, coherent with the 'tidyverse' design philosophy, for performing basic statistical tests, including t-test, Wilcoxon test, ANOVA, Kruskal-Wallis and correlation analyses. The output of each test is automatically transformed into a tidy data frame to facilitate visualization. Additional functions are available for reshaping, reordering, manipulating and visualizing correlation matrix. Functions are also included to facilitate the analysis of factorial experiments, including purely 'within-Ss' designs (repeated measures), purely 'between-Ss' designs, and mixed 'within-and-between-Ss' designs. It's also possible to compute several effect size metrics, including "eta squared" for ANOVA, "Cohen's d" for t-test and 'Cramer V' for the association between categorical variables. The package contains helper functions for identifying univariate and multivariate outliers, assessing normality and homogeneity of variances.

Maintained by Alboukadel Kassambara. Last updated 2 years ago.

458 stars 15.27 score 11k scripts 432 dependents

asa12138

pcutils:Some Useful Functions for Statistics and Visualization

Offers a range of utilities and functions for everyday programming tasks. 1.Data Manipulation. Such as grouping and merging, column splitting, and character expansion. 2.File Handling. Read and convert files in popular formats. 3.Plotting Assistance. Helpful utilities for generating color palettes, validating color formats, and adding transparency. 4.Statistical Analysis. Includes functions for pairwise comparisons and multiple testing corrections, enabling perform statistical analyses with ease. 5.Graph Plotting, Provides efficient tools for creating doughnut plot and multi-layered doughnut plot; Venn diagrams, including traditional Venn diagrams, upset plots, and flower plots; Simplified functions for creating stacked bar plots, or a box plot with alphabets group for multiple comparison group.

Maintained by Chen Peng. Last updated 7 days ago.

22 stars 6.47 score 28 scripts 4 dependents

rkabacoff

qacBase:Functions to Facilitate Exploratory Data Analysis

Functions for descriptive statistics, data management, and data visualization.

Maintained by Kabacoff Robert. Last updated 3 years ago.

edastatistics

1 stars 5.13 score 45 scripts

bioc

barcodetrackR:Functions for Analyzing Cellular Barcoding Data

barcodetrackR is an R package developed for the analysis and visualization of clonal tracking data. Data required is samples and tag abundances in matrix form. Usually from cellular barcoding experiments, integration site retrieval analyses, or similar technologies.

Maintained by Diego Alexander Espinoza. Last updated 5 months ago.

softwarevisualizationsequencing

5 stars 4.70 score 6 scripts

ropensci

cRegulome:Obtain and Visualize Regulome-Gene Expression Correlations in Cancer

Builds a 'SQLite' database file of pre-calculated transcription factor/microRNA-gene correlations (co-expression) in cancer from the Cistrome Cancer Liu et al. (2011) <doi:10.1186/gb-2011-12-8-r83> and 'miRCancerdb' databases (in press). Provides custom classes and functions to query, tidy and plot the correlation data.

Maintained by Mahmoud Ahmed. Last updated 5 years ago.

cancer-genomicsdatabasedatasciencemicrornapeer-reviewedtcga-datatranscription-factors

3 stars 3.69 score 54 scripts

fanhansen

creditmodel:Toolkit for Credit Modeling, Analysis and Visualization

Provides a highly efficient R tool suite for Credit Modeling, Analysis and Visualization.Contains infrastructure functionalities such as data exploration and preparation, missing values treatment, outliers treatment, variable derivation, variable selection, dimensionality reduction, grid search for hyper parameters, data mining and visualization, model evaluation, strategy analysis etc. This package is designed to make the development of binary classification models (machine learning based models as well as credit scorecard) simpler and faster. The references including: 1 Refaat, M. (2011, ISBN: 9781447511199). Credit Risk Scorecard: Development and Implementation Using SAS; 2 Bezdek, James C.FCM: The fuzzy c-means clustering algorithm. Computers & Geosciences (0098-3004),<DOI:10.1016/0098-3004(84)90020-7>.

Maintained by Dongping Fan. Last updated 3 years ago.

4 stars 3.48 score 15 scripts

abeyrann

Path.Analysis:Path Coefficient Analysis

Facilitates the performance of several analyses, including simple and sequential path coefficient analysis, correlation estimate, drawing correlogram, Heatmap, and path diagram. When working with raw data, that includes one or more dependent variables along with one or more independent variables are available, the path coefficient analysis can be conducted. It allows for testing direct effects, which can be a vital indicator in path coefficient analysis. The process of preparing the dataset rule is explained in detail in the vignette file "Path.Analysis_manual.Rmd". You can find this in the folders labelled "data" and "~/inst/extdata". Also see: 1)the 'lavaan', 2)a sample of sequential path analysis in 'metan' suggested by Olivoto and Lúcio (2020) <doi:10.1111/2041-210X.13384>, 3)the simple 'PATHSAS' macro written in 'SAS' by Cramer et al. (1999) <doi:10.1093/jhered/90.1.260>, and 4)the semPlot() function of 'OpenMx' as initial tools for conducting path coefficient analyses and SEM (Structural Equation Modeling). To gain a comprehensive understanding of path coefficient analysis, both in theory and practice, see a 'Minitab' macro developed by Arminian, A. in the paper by Arminian et al. (2008) <doi:10.1080/15427520802043182>.

Maintained by Ali Arminian. Last updated 6 months ago.

2.70 score

hanjunwei-lab

iPRISM:Intelligent Predicting Response to Cancer Immunotherapy Through Systematic Modeling

Immunotherapy has revolutionized cancer treatment, but predicting patient response remains challenging. Here, we presented Intelligent Predicting Response to cancer Immunotherapy through Systematic Modeling (iPRISM), a novel network-based model that integrates multiple data types to predict immunotherapy outcomes. It incorporates gene expression, biological functional network, tumor microenvironment characteristics, immune-related pathways, and clinical data to provide a comprehensive view of factors influencing immunotherapy efficacy. By identifying key genetic and immunological factors, it provides an insight for more personalized treatment strategies and combination therapies to overcome resistance mechanisms.

Maintained by Junwei Han. Last updated 9 months ago.

2.00 score

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